10.5061/DRYAD.B2RBNZSBN
Prince, Kimberly
0000-0001-5139-9224
University of Florida
A global, cross-system meta-analysis of polychlorinated biphenyl
biomagnification
Dryad
dataset
2020
2020-12-03T00:00:00Z
2020-12-03T00:00:00Z
en
171063 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Studies evaluating the mechanisms underpinning the biomagnification of
polychlorinated biphenyls (PCBs), a globally prevalent group of regulated
persistent organic pollutants, commonly couple chemical and stable isotope
analyses to identify bioaccumulation pathways. Due to analytical costs
constraining the taxonomic and geographic scope, sample size, and the
range of compounds analyzed for most studies, and study-to-study variation
in methodologies and analytical resolution, how PCBs biomagnify at food
web, regional, and global scales remains uncertain. To overcome these
constraints, we compiled diet (stable isotopes) and lipid-normalized PCB
data from peer-reviewed studies reporting both values and used
complementary analyses to evaluate the relative importance of global key
PCB drivers and assess ecosystem- and ocean-wide biomagnification trends
of sum total PCB concentrations (PCBST), and the concentrations of seven
individual PCB congeners, and their sum (PCBå7). We discovered that the
number of congeners analyzed, region, and class were the most important
factors predicting PCBST, while, similarly, region, class and feeding
location were the best predictors of PCBå7 and all seven congeners. In
addition, biomagnification analyses revealed that PCBST, PCBΣ7 and the
seven individual PCBs all demonstrate a higher propensity for
biomagnification in marine relative to freshwater food webs and within the
Atlantic Ocean relative to the Pacific. We further found that some
congeners exhibiting relatively high trophic magnification factors (TMFs)
in the Atlantic exhibited low TMFs in the Pacific (such as PCB 118), while
the order of individual congener TMFs relative to one another remained
consistent across marine and freshwater ecosystems. Our analyses
demonstrate that novel insights regarding PCB concentrations across
taxonomic, food webs, regional and global scales can be gleaned by
leveraging existing data to overcome analytical constraints.
To synthesize stable isotope and PCB data, we conducted a search in April
2019 on Web of Science (www.webofknowledge.com) using topic words
“polychlorinated biphenyl* or PCB*” AND “stable isotope*” with no time
restriction. To expand the coverage of this search, we applied the same
search criteria and incorporated any additional studies from the following
journals that commonly publish on this topic: Environmental Research,
Chemosphere, PLOS one, Environmental Toxicology and Chemistry,
Environmental Science and Technology, Science of the Total Environment,
Environmental Pollution, Ecotoxicology and Environmental Safety, Marine
Pollution Bulletin, Journal of Wildlife Diseases, Marine Ecology Progress
Series, Environmental Science Pollution and Restoration,Chemistry and
Ecology, Environment International, and Frontiers in Ecology and the
Environment. Additional studies referenced within studies identified in
our initial search were also incorporated in our database. Across all
studies, we only included those that published original data and reported
both PCB concentration and stable isotope data, resulting in a total of
231 studies published between 1995 and 2019. Since studies varied in
reported units of measurement, we further reduced this database to the
subset of 73 studies that reported PCB concentrations in ng/g
lipid-normalized weight (lw). In these studies, we extracted 815
unique sum total PCB concentration (PCBST) values, i.e. the summed
concentration of all congeners measured for a given sample. PCBST was
overwhelmingly the most commonly reported response variable, and only 31
of the 73 studies provided concentrations for individual congeners in
either the main text or supplement files in addition to PCBST values. In
the few studies that only reported individual PCB congener concentrations,
we calculated PCBST ourselves. We herein present analyses focused on
evaluating the relative importance of different drivers of PCBST, as a
measure of aggregate PCB exposure in line with prior studies34–36, at
global, regional, and food web scales given the large and geographically
well-distributed characteristics of this dataset (Fig. 1). In
our PCBST analyses we include the number of congeners analyzed an
explanatory variable to help control for between-study differences in this
important dimension of analytical effort (see Data Analysis below). Though
details on which congeners analyzed were available, without concentrations
of each congener we were unable to include them in the analysis. Thus, the
total number of congeners analyzed in each study was used. We complement
the PCBST dataset with one in which the number and composition of
congeners analyzed are standardized. In this second dataset, we focus only
on the seven most commonly analyzed and reported PCB congeners (PCBs 28,
52, 101, 118, 138, 180, 153) as well as their sum (PCBΣ7), which we
derived from 13 of the 73 studies. This particular set of seven congeners
happens to be the full suite of indicator PCBs recommended for monitoring
by the International Council for the Exploration of the Sea (ICES) given
their high concentrations in commercial mixtures and wide chlorination
range . For each dataset, we then categorized samples by geographic
region (i.e. country and, if relevant, continent or ocean including
associated seas and bays) and ecosystem type: marine, freshwater, brackish
(mix of freshwater and saltwater), terrestrial and multiple systems (if
the sampled organism utilizes multiple ecosystems). We also cataloged
geographical coordinates when available and, if not provided, estimated
them based on the study site description using Google Maps. In addition,
we recorded the taxonomic family of each sample (class), the type of
tissue sampled (i.e. fat, blubber, blood, red blood cells, plasma, egg,
embryo, heart, kidney, liver, muscle, milk, spleen, pylori, whole body or
multiple tissues), the composition and total number of congeners analyzed,
and relevant characteristics of the sampled organism including its
biomass, age, and trophic level. If trophic level was not provided, we
assigned the sampled species a trophic level using Fishbase.org or used
the organism’s consumer status, which we based on published information
about its diet. Primary producer, primary consumer, secondary consumer,
tertiary consumer, or apex predator organisms corresponded to trophic
levels 1-5. To assess the validity of the assigned trophic levels, we
first fit a linear model between isotopic nitrogen and trophic level using
252 samples for which both values were reported in the study. We then
found that 92% of the assigned trophic level values for the remaining 526
samples in our data set were within the 95% prediction interval of this
linear model, indicating that they correspond well with study-derived
trophic levels. We further classified organisms by feeding location
(organisms occurring exclusively within aquatic systems were categorized
as benthic, demersal, benthopelagic, pelagic, bathypelagic, or
bathy-demersal and primarily land-based species were categorized as:
exclusively-terrestrial (e.g. sparrows, spiders, owls, moths, pigeon),
terrestrial/marine (e.g. Artic foxes, Polar bears, herring gull), or
terrestrial/freshwater (dragonflies, amphibians, ducks), and by feeding
behavior (filter feeder, deposit feeder, autotroph, herbivore, carnivore,
or omnivore). When necessary, we used Web Plot Digitizer
(https://automeris.io/WebPlotDigitizer/) to extract isotope data, trophic
level, and PCB concentration data.
To push PCB biomagnification analyses toward the forefront of the
ecotoxicology field, future studies can continue to add to this database
through the reporting of PCB and stable isotope data using standardized
methods and units of measurement (e.g. lipid-normalized concentrations),
thereby collectively advancing knowledge on the global transport and fate
of PCB concentrations. Further, we encourage others to leverage the data
compiled in our existing database to contextualize their findings relative
to other organisms sampled in a similar region or from a taxonomic class.
Such communal datasets provide one of the few comprehensive tools to
understand and manage how PCBs move into and across ecosystems at the
global scale.